import pandas as pd
import numpy as np

import plotly.express as px

from IPython.core.display import display, HTML

imported_df = pd.read_csv('listings.csv')

df = imported_df.copy()

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 16055 entries, 0 to 16054
Data columns (total 18 columns):
 #   Column                          Non-Null Count  Dtype  
---  ------                          --------------  -----  
 0   id                              16055 non-null  int64  
 1   name                            16045 non-null  object 
 2   host_id                         16055 non-null  int64  
 3   host_name                       16014 non-null  object 
 4   neighbourhood_group             16055 non-null  object 
 5   neighbourhood                   16055 non-null  object 
 6   latitude                        16055 non-null  float64
 7   longitude                       16055 non-null  float64
 8   room_type                       16055 non-null  object 
 9   price                           16055 non-null  int64  
 10  minimum_nights                  16055 non-null  int64  
 11  number_of_reviews               16055 non-null  int64  
 12  last_review                     11772 non-null  object 
 13  reviews_per_month               11772 non-null  float64
 14  calculated_host_listings_count  16055 non-null  int64  
 15  availability_365                16055 non-null  int64  
 16  number_of_reviews_ltm           16055 non-null  int64  
 17  license                         10277 non-null  object 
dtypes: float64(3), int64(8), object(7)
memory usage: 2.2+ MB

Show number of missing values:

df.isna().sum()

id                                   0
name                                10
host_id                              0
host_name                           41
neighbourhood_group                  0
neighbourhood                        0
latitude                             0
longitude                            0
room_type                            0
price                                0
minimum_nights                       0
number_of_reviews                    0
last_review                       4283
reviews_per_month                 4283
calculated_host_listings_count       0
availability_365                     0
number_of_reviews_ltm                0
license                           5778
dtype: int64

df.describe()

	id	host_id	latitude	longitude	price	minimum_nights	number_of_reviews	reviews_per_month	calculated_host_listings_count	availability_365	number_of_reviews_ltm
count	1.605500e+04	1.605500e+04	16055.000000	16055.000000	16055.000000	16055.000000	16055.000000	11772.000000	16055.000000	16055.000000	16055.000000
mean	2.822959e+07	1.173823e+08	41.391995	2.167132	112.467331	12.970103	34.153348	1.164569	17.319028	164.437745	3.762940
std	1.594047e+07	1.232569e+08	0.014416	0.017599	325.455790	31.696885	67.383300	1.439324	33.352697	136.654295	8.811417
min	1.867400e+04	3.073000e+03	41.333420	2.091590	0.000000	1.000000	0.000000	0.010000	1.000000	0.000000	0.000000
25%	1.504661e+07	8.813134e+06	41.380800	2.157060	40.000000	1.000000	0.000000	0.180000	1.000000	8.000000	0.000000
50%	2.956314e+07	5.913604e+07	41.389450	2.168150	70.000000	3.000000	5.000000	0.700000	3.000000	153.000000	0.000000
75%	4.231742e+07	2.131051e+08	41.401520	2.177390	125.000000	30.000000	35.000000	1.670000	16.000000	306.000000	4.000000
max	5.270268e+07	4.257934e+08	41.461930	2.229670	9999.000000	1124.000000	862.000000	27.000000	182.000000	365.000000	283.000000

df.head()

	id	name	host_id	host_name	neighbourhood_group	neighbourhood	latitude	longitude	room_type	price	minimum_nights	number_of_reviews	last_review	reviews_per_month	calculated_host_listings_count	availability_365	number_of_reviews_ltm	license
0	18674	Huge flat for 8 people close to Sagrada Familia	71615	Mireia And Maria	Eixample	la Sagrada Família	41.40420	2.17306	Entire home/apt	121	1	21	2019-10-11	0.23	19	47	0	HUTB-002062
1	23197	Forum CCIB DeLuxe★Spacious &Elegant★Large Balcony	90417	Etain (Marnie)	Sant Martí	el Besòs i el Maresme	41.41291	2.22063	Entire home/apt	220	4	52	2019-12-15	0.74	2	86	0	HUTB-005057
2	32711	Sagrada Familia area - Còrsega 1	135703	Nick	Gràcia	el Camp d'en Grassot i Gràcia Nova	41.40566	2.17015	Entire home/apt	144	2	63	2019-09-06	0.60	3	85	0	HUTB-001722
3	34981	VIDRE HOME PLAZA REAL on LAS RAMBLAS	73163	Andres	Ciutat Vella	el Barri Gòtic	41.37978	2.17623	Entire home/apt	181	4	156	2020-03-11	1.55	2	136	7	HUTB-001506
4	35379	Double 04 CasanovaRooms Barcelona	152232	Pablo	Eixample	l'Antiga Esquerra de l'Eixample	41.39036	2.15274	Private room	41	2	358	2021-07-21	4.01	4	193	41	Exempt

import plotly.express as px

fig = px.scatter_mapbox(df, lat="latitude", lon="longitude",
                        color_discrete_sequence=["blue"], zoom=12.5, height=800, opacity=1,
                       title='Airbnb properties in Barcelona')
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r":0,"t":30,"l":0,"b":0})
fig.show()

fig = px.scatter_mapbox(df, lat="latitude", lon="longitude",
                        color='room_type', zoom=12.5, height=800, opacity=1,
                       title='Airbnb properties in Barcelona')
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r":0,"t":30,"l":0,"b":0})
fig.show()

for district in df.neighbourhood.value_counts().index[:1]:
    dss = df[df.neighbourhood == district]
    dssvc = dss.room_type.value_counts()
    n = len(dssvc.index)
    
    labels = dssvc.index.to_list()
    parents = ([district]*n),
    parents = parents[0]
    vals = dssvc.to_list()

for district in df.neighbourhood.value_counts().index[1:5]:
    dss = df[df.neighbourhood == district]
    dssvc = dss.room_type.value_counts()
    n = len(dssvc.index)
    
    labels += dssvc.index.to_list()
    addparents = [district]*n,
    parents += addparents[0]
    vals = vals + dssvc.to_list()

df_top_district = pd.DataFrame([labels, vals, parents])
df_top_district = df_top_district.T
df_top_district.columns = ['type','eff','district']
df_top_district

	type	eff	district
0	Entire home/apt	1285	la Dreta de l'Eixample
1	Private room	599	la Dreta de l'Eixample
2	Hotel room	70	la Dreta de l'Eixample
3	Shared room	41	la Dreta de l'Eixample
4	Private room	653	el Raval
5	Entire home/apt	569	el Raval
6	Hotel room	9	el Raval
7	Shared room	8	el Raval
8	Entire home/apt	551	el Barri Gòtic
9	Private room	529	el Barri Gòtic
10	Shared room	13	el Barri Gòtic
11	Hotel room	1	el Barri Gòtic
12	Entire home/apt	534	Sant Pere, Santa Caterina i la Ribera
13	Private room	405	Sant Pere, Santa Caterina i la Ribera
14	Hotel room	18	Sant Pere, Santa Caterina i la Ribera
15	Shared room	11	Sant Pere, Santa Caterina i la Ribera
16	Entire home/apt	631	la Sagrada Família
17	Private room	323	la Sagrada Família
18	Hotel room	5	la Sagrada Família
19	Shared room	4	la Sagrada Família

fig = px.sunburst(df_top_district, path=['district','type'], values='eff' )
# Update layout for tight margin
# See https://plot.ly/python/creating-and-updating-figures/
fig.update_layout(margin = dict(t=30, l=0, r=0, b=0))
fig.update_layout(title_text="Room number per district and room type")
fig.show()

df_top_district['percent'] = df_top_district.apply(lambda x: 100 * x.eff / df[df.neighbourhood==x.district]['id'].count() , axis=1)
df_top_district

	type	eff	district	percent
0	Entire home/apt	1285	la Dreta de l'Eixample	64.411028
1	Private room	599	la Dreta de l'Eixample	30.025063
2	Hotel room	70	la Dreta de l'Eixample	3.508772
3	Shared room	41	la Dreta de l'Eixample	2.055138
4	Private room	653	el Raval	52.703793
5	Entire home/apt	569	el Raval	45.924132
6	Hotel room	9	el Raval	0.726392
7	Shared room	8	el Raval	0.645682
8	Entire home/apt	551	el Barri Gòtic	50.365631
9	Private room	529	el Barri Gòtic	48.354662
10	Shared room	13	el Barri Gòtic	1.188300
11	Hotel room	1	el Barri Gòtic	0.091408
12	Entire home/apt	534	Sant Pere, Santa Caterina i la Ribera	55.165289
13	Private room	405	Sant Pere, Santa Caterina i la Ribera	41.838843
14	Hotel room	18	Sant Pere, Santa Caterina i la Ribera	1.859504
15	Shared room	11	Sant Pere, Santa Caterina i la Ribera	1.136364
16	Entire home/apt	631	la Sagrada Família	65.524403
17	Private room	323	la Sagrada Família	33.541018
18	Hotel room	5	la Sagrada Família	0.519211
19	Shared room	4	la Sagrada Família	0.415369

fig1 = px.bar(
    df_top_district,
    x='district',
    y='eff',
    facet_col='type',
    width=300*5,
    color='type'
)
fig2 = px.bar(
    df_top_district,
    x='type',
    y='percent',
    facet_col='district',
    width=300*5,
    color='type'
)
fig3 = px.bar(
    df_top_district,
    facet_col='type',
    y='percent',
    x='district',
    width=300*5,
    color='type'
)



fig1.update_layout(title_text="Room number per type per district")
fig1.update_xaxes(title_text="")

fig2.update_xaxes(title_text="")
fig2.update_layout(title_text="Room type proportion per district")

fig3.update_xaxes(title_text="")
fig3.update_layout(title_text="Room type proportion comparison per district")

display(HTML('<h2>Room type analysis the first 5 districts</h2>'))
fig1.show()
fig2.show()
fig3.show()

Room type analysis the first 5 districts